Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations50000
Missing cells16631
Missing cells (%)1.0%
Duplicate rows108
Duplicate rows (%)0.2%
Total size in memory8.2 MiB
Average record size in memory173.0 B

Variable types

Categorical8
Numeric10
Text1
Boolean13

Alerts

Visibility(mi) has constant value "10.0"Constant
Turning_Loop has constant value "False"Constant
Start_Year has constant value "2016.0"Constant
Is_Weekend has constant value "0.0"Constant
Dataset has 108 (0.2%) duplicate rowsDuplicates
Bump is highly overall correlated with Traffic_CalmingHigh correlation
Humidity(%) is highly overall correlated with Temperature(F)High correlation
Start_Lat is highly overall correlated with Start_LngHigh correlation
Start_Lng is highly overall correlated with Start_LatHigh correlation
Temperature(F) is highly overall correlated with Humidity(%)High correlation
Traffic_Calming is highly overall correlated with BumpHigh correlation
Source is highly imbalanced (99.4%)Imbalance
Weather_Condition is highly imbalanced (59.2%)Imbalance
Amenity is highly imbalanced (93.5%)Imbalance
Bump is highly imbalanced (99.4%)Imbalance
Crossing is highly imbalanced (65.1%)Imbalance
Give_Way is highly imbalanced (98.2%)Imbalance
Junction is highly imbalanced (51.9%)Imbalance
No_Exit is highly imbalanced (99.4%)Imbalance
Railway is highly imbalanced (90.5%)Imbalance
Roundabout is highly imbalanced (99.9%)Imbalance
Station is highly imbalanced (79.8%)Imbalance
Stop is highly imbalanced (78.7%)Imbalance
Traffic_Calming is highly imbalanced (99.2%)Imbalance
Traffic_Signal is highly imbalanced (50.5%)Imbalance
Temperature(F) has 817 (1.6%) missing valuesMissing
Humidity(%) has 937 (1.9%) missing valuesMissing
Pressure(in) has 679 (1.4%) missing valuesMissing
Visibility(mi) has 936 (1.9%) missing valuesMissing
Wind_Direction has 553 (1.1%) missing valuesMissing
Wind_Speed(mph) has 11878 (23.8%) missing valuesMissing
Weather_Condition has 831 (1.7%) missing valuesMissing
Start_Hour has 1122 (2.2%) zerosZeros
Start_Weekday has 7956 (15.9%) zerosZeros

Reproduction

Analysis started2025-01-10 19:11:43.870543
Analysis finished2025-01-10 19:11:54.509410
Duration10.64 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Source
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Source2
49975 
Source3
 
25

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters350000
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSource2
2nd rowSource2
3rd rowSource2
4th rowSource2
5th rowSource2

Common Values

ValueCountFrequency (%)
Source2 49975
> 99.9%
Source3 25
 
0.1%

Length

2025-01-11T00:41:54.679504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-11T00:41:54.731279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
source2 49975
> 99.9%
source3 25
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 50000
14.3%
o 50000
14.3%
u 50000
14.3%
r 50000
14.3%
c 50000
14.3%
e 50000
14.3%
2 49975
14.3%
3 25
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 250000
71.4%
Uppercase Letter 50000
 
14.3%
Decimal Number 50000
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 50000
20.0%
u 50000
20.0%
r 50000
20.0%
c 50000
20.0%
e 50000
20.0%
Decimal Number
ValueCountFrequency (%)
2 49975
> 99.9%
3 25
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
S 50000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 300000
85.7%
Common 50000
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 50000
16.7%
o 50000
16.7%
u 50000
16.7%
r 50000
16.7%
c 50000
16.7%
e 50000
16.7%
Common
ValueCountFrequency (%)
2 49975
> 99.9%
3 25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 350000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 50000
14.3%
o 50000
14.3%
u 50000
14.3%
r 50000
14.3%
c 50000
14.3%
e 50000
14.3%
2 49975
14.3%
3 25
 
< 0.1%

Severity
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2
27597 
3
22343 
1
 
47
4
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 27597
55.2%
3 22343
44.7%
1 47
 
0.1%
4 13
 
< 0.1%

Length

2025-01-11T00:41:54.775127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-11T00:41:54.819272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 27597
55.2%
3 22343
44.7%
1 47
 
0.1%
4 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 27597
55.2%
3 22343
44.7%
1 47
 
0.1%
4 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 27597
55.2%
3 22343
44.7%
1 47
 
0.1%
4 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 50000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 27597
55.2%
3 22343
44.7%
1 47
 
0.1%
4 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 27597
55.2%
3 22343
44.7%
1 47
 
0.1%
4 13
 
< 0.1%

Start_Lat
Real number (ℝ)

HIGH CORRELATION 

Distinct24486
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.641439
Minimum32.542587
Maximum41.428753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-01-11T00:41:54.870994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.542587
5-th percentile32.949959
Q133.957836
median34.171249
Q337.783509
95-th percentile38.65414
Maximum41.428753
Range8.886166
Interquartile range (IQR)3.8256732

Descriptive statistics

Standard deviation2.1052482
Coefficient of variation (CV)0.059067431
Kurtosis-1.611048
Mean35.641439
Median Absolute Deviation (MAD)0.958225
Skewness0.31361865
Sum1782071.9
Variance4.4320701
MonotonicityNot monotonic
2025-01-11T00:41:54.927739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.808498 83
 
0.2%
33.876289 65
 
0.1%
33.941364 62
 
0.1%
34.010056 61
 
0.1%
33.99345 59
 
0.1%
33.99704 51
 
0.1%
34.034126 50
 
0.1%
34.019634 49
 
0.1%
33.864437 47
 
0.1%
34.034149 46
 
0.1%
Other values (24476) 49427
98.9%
ValueCountFrequency (%)
32.542587 1
< 0.1%
32.543114 1
< 0.1%
32.543251 1
< 0.1%
32.543369 2
< 0.1%
32.543938 1
< 0.1%
32.544304 2
< 0.1%
32.547222 2
< 0.1%
32.547546 1
< 0.1%
32.548813 1
< 0.1%
32.549324 1
< 0.1%
ValueCountFrequency (%)
41.428753 1
< 0.1%
41.424313 1
< 0.1%
41.423275 1
< 0.1%
41.42181 1
< 0.1%
41.420975 1
< 0.1%
41.420818 1
< 0.1%
41.420666 1
< 0.1%
41.420506 1
< 0.1%
41.420448 1
< 0.1%
41.414131 1
< 0.1%

Start_Lng
Real number (ℝ)

HIGH CORRELATION 

Distinct23971
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.54061
Minimum-123.81075
Maximum-112.04735
Zeros0
Zeros (%)0.0%
Negative50000
Negative (%)100.0%
Memory size781.2 KiB
2025-01-11T00:41:54.987777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-123.81075
5-th percentile-122.39275
Q1-121.84113
median-118.39246
Q3-117.92166
95-th percentile-117.10496
Maximum-112.04735
Range11.763398
Interquartile range (IQR)3.9194732

Descriptive statistics

Standard deviation2.1073851
Coefficient of variation (CV)-0.01762903
Kurtosis-0.72849601
Mean-119.54061
Median Absolute Deviation (MAD)1.236672
Skewness0.039279023
Sum-5977030.7
Variance4.4410718
MonotonicityNot monotonic
2025-01-11T00:41:55.052664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-112.0473547 361
 
0.7%
-122.366852 83
 
0.2%
-118.102577 63
 
0.1%
-118.096634 62
 
0.1%
-117.823219 61
 
0.1%
-118.125191 60
 
0.1%
-118.069351 59
 
0.1%
-118.368263 56
 
0.1%
-117.931015 51
 
0.1%
-118.027214 50
 
0.1%
Other values (23961) 49094
98.2%
ValueCountFrequency (%)
-123.810753 1
< 0.1%
-123.808311 1
< 0.1%
-123.798073 1
< 0.1%
-123.793976 1
< 0.1%
-123.793404 1
< 0.1%
-123.788673 1
< 0.1%
-123.768166 1
< 0.1%
-123.625793 1
< 0.1%
-123.547714 1
< 0.1%
-123.454147 1
< 0.1%
ValueCountFrequency (%)
-112.0473547 361
0.7%
-116.26667 1
 
< 0.1%
-116.266754 1
 
< 0.1%
-116.266762 1
 
< 0.1%
-116.275871 1
 
< 0.1%
-116.276688 2
 
< 0.1%
-116.284142 1
 
< 0.1%
-116.290718 1
 
< 0.1%
-116.291046 1
 
< 0.1%
-116.291321 1
 
< 0.1%

Distance(mi)
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0.0
26196 
0.01
23660 
0.025
 
142
0.02
 
2

Length

Max length5
Median length3
Mean length3.47892
Min length3

Characters and Unicode

Total characters173946
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.01
2nd row0.0
3rd row0.0
4th row0.0
5th row0.01

Common Values

ValueCountFrequency (%)
0.0 26196
52.4%
0.01 23660
47.3%
0.025 142
 
0.3%
0.02 2
 
< 0.1%

Length

2025-01-11T00:41:55.114043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-11T00:41:55.160183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 26196
52.4%
0.01 23660
47.3%
0.025 142
 
0.3%
0.02 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 100000
57.5%
. 50000
28.7%
1 23660
 
13.6%
2 144
 
0.1%
5 142
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123946
71.3%
Other Punctuation 50000
28.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 100000
80.7%
1 23660
 
19.1%
2 144
 
0.1%
5 142
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 50000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 173946
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 100000
57.5%
. 50000
28.7%
1 23660
 
13.6%
2 144
 
0.1%
5 142
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 173946
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 100000
57.5%
. 50000
28.7%
1 23660
 
13.6%
2 144
 
0.1%
5 142
 
0.1%

Street
Text

Distinct6375
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2025-01-11T00:41:55.241345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length32
Mean length10.77334
Min length3

Characters and Unicode

Total characters538667
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3541 ?
Unique (%)7.1%

Sample

1st rowN Lake Ave
2nd rowDewey Dr
3rd rowI-10 E
4th rowI-680 S
5th rowAltamont Pass Rd
ValueCountFrequency (%)
s 10244
 
8.1%
n 9624
 
7.6%
fwy 9007
 
7.1%
e 6978
 
5.5%
w 6816
 
5.4%
rd 5156
 
4.1%
ave 5142
 
4.1%
blvd 3342
 
2.7%
st 2482
 
2.0%
san 1811
 
1.4%
Other values (4144) 65492
51.9%
2025-01-11T00:41:55.398575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
76094
 
14.1%
a 28791
 
5.3%
e 28526
 
5.3%
o 21182
 
3.9%
S 20157
 
3.7%
n 19895
 
3.7%
- 19157
 
3.6%
r 18112
 
3.4%
l 17879
 
3.3%
i 16802
 
3.1%
Other values (55) 272072
50.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 264415
49.1%
Uppercase Letter 131366
24.4%
Space Separator 76094
 
14.1%
Decimal Number 47629
 
8.8%
Dash Punctuation 19157
 
3.6%
Other Punctuation 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 28791
10.9%
e 28526
10.8%
o 21182
 
8.0%
n 19895
 
7.5%
r 18112
 
6.8%
l 17879
 
6.8%
i 16802
 
6.4%
y 16085
 
6.1%
d 15866
 
6.0%
t 15827
 
6.0%
Other values (16) 65450
24.8%
Uppercase Letter
ValueCountFrequency (%)
S 20157
15.3%
I 12691
9.7%
A 12529
9.5%
F 11293
8.6%
N 10704
8.1%
C 8998
 
6.8%
W 8514
 
6.5%
E 8492
 
6.5%
R 7849
 
6.0%
B 6167
 
4.7%
Other values (16) 23972
18.2%
Decimal Number
ValueCountFrequency (%)
0 11751
24.7%
1 9626
20.2%
5 7914
16.6%
8 6992
14.7%
2 2753
 
5.8%
4 2494
 
5.2%
9 2171
 
4.6%
6 1887
 
4.0%
7 1249
 
2.6%
3 792
 
1.7%
Space Separator
ValueCountFrequency (%)
76094
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19157
100.0%
Other Punctuation
ValueCountFrequency (%)
' 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 395781
73.5%
Common 142886
 
26.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 28791
 
7.3%
e 28526
 
7.2%
o 21182
 
5.4%
S 20157
 
5.1%
n 19895
 
5.0%
r 18112
 
4.6%
l 17879
 
4.5%
i 16802
 
4.2%
y 16085
 
4.1%
d 15866
 
4.0%
Other values (42) 192486
48.6%
Common
ValueCountFrequency (%)
76094
53.3%
- 19157
 
13.4%
0 11751
 
8.2%
1 9626
 
6.7%
5 7914
 
5.5%
8 6992
 
4.9%
2 2753
 
1.9%
4 2494
 
1.7%
9 2171
 
1.5%
6 1887
 
1.3%
Other values (3) 2047
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538667
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
76094
 
14.1%
a 28791
 
5.3%
e 28526
 
5.3%
o 21182
 
3.9%
S 20157
 
3.7%
n 19895
 
3.7%
- 19157
 
3.6%
r 18112
 
3.4%
l 17879
 
3.3%
i 16802
 
3.1%
Other values (55) 272072
50.5%

Temperature(F)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct368
Distinct (%)0.7%
Missing817
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean66.635176
Minimum32.25
Maximum100.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-01-11T00:41:55.470397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.25
5-th percentile46.9
Q157.9
median66
Q375
95-th percentile89.1
Maximum100.65
Range68.4
Interquartile range (IQR)17.1

Descriptive statistics

Standard deviation12.579588
Coefficient of variation (CV)0.18878299
Kurtosis-0.12429023
Mean66.635176
Median Absolute Deviation (MAD)8.8
Skewness0.14966728
Sum3277317.8
Variance158.24603
MonotonicityNot monotonic
2025-01-11T00:41:55.532161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 1767
 
3.5%
59 1699
 
3.4%
77 1213
 
2.4%
66 1195
 
2.4%
63 1190
 
2.4%
64.9 1182
 
2.4%
64 1166
 
2.3%
66.9 1155
 
2.3%
57.9 1128
 
2.3%
60.1 1128
 
2.3%
Other values (358) 36360
72.7%
ValueCountFrequency (%)
32.25 221
0.4%
32.4 2
 
< 0.1%
32.5 1
 
< 0.1%
33.1 25
 
0.1%
33.3 2
 
< 0.1%
33.4 1
 
< 0.1%
33.6 1
 
< 0.1%
33.8 21
 
< 0.1%
34 19
 
< 0.1%
34.5 1
 
< 0.1%
ValueCountFrequency (%)
100.65 164
0.3%
100.6 1
 
< 0.1%
100.4 47
 
0.1%
100 32
 
0.1%
99.9 1
 
< 0.1%
99.5 1
 
< 0.1%
99.1 1
 
< 0.1%
99 50
 
0.1%
98.8 3
 
< 0.1%
98.6 65
 
0.1%

Humidity(%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct97
Distinct (%)0.2%
Missing937
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean59.861912
Minimum4
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-01-11T00:41:55.599056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile18
Q143
median62
Q378
95-th percentile94
Maximum100
Range96
Interquartile range (IQR)35

Descriptive statistics

Standard deviation23.162895
Coefficient of variation (CV)0.38693877
Kurtosis-0.76329373
Mean59.861912
Median Absolute Deviation (MAD)17
Skewness-0.30566259
Sum2937005
Variance536.5197
MonotonicityNot monotonic
2025-01-11T00:41:55.658978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1381
 
2.8%
78 1164
 
2.3%
93 1147
 
2.3%
81 1060
 
2.1%
73 1034
 
2.1%
87 1020
 
2.0%
72 995
 
2.0%
68 988
 
2.0%
75 974
 
1.9%
83 914
 
1.8%
Other values (87) 38386
76.8%
(Missing) 937
 
1.9%
ValueCountFrequency (%)
4 23
 
< 0.1%
5 24
 
< 0.1%
6 46
 
0.1%
7 66
 
0.1%
8 97
 
0.2%
9 131
0.3%
10 148
0.3%
11 176
0.4%
12 236
0.5%
13 268
0.5%
ValueCountFrequency (%)
100 1381
2.8%
99 18
 
< 0.1%
98 4
 
< 0.1%
97 298
 
0.6%
96 413
 
0.8%
95 2
 
< 0.1%
94 525
 
1.1%
93 1147
2.3%
92 109
 
0.2%
91 19
 
< 0.1%

Pressure(in)
Real number (ℝ)

MISSING 

Distinct70
Distinct (%)0.1%
Missing679
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean29.975349
Minimum29.635
Maximum30.315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-01-11T00:41:55.716898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29.635
5-th percentile29.79
Q129.89
median29.97
Q330.06
95-th percentile30.18
Maximum30.315
Range0.68
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.12605859
Coefficient of variation (CV)0.0042054084
Kurtosis0.46405979
Mean29.975349
Median Absolute Deviation (MAD)0.08
Skewness0.0059101948
Sum1478414.2
Variance0.015890767
MonotonicityNot monotonic
2025-01-11T00:41:55.777690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.94 1855
 
3.7%
29.96 1840
 
3.7%
29.91 1759
 
3.5%
29.88 1661
 
3.3%
29.99 1641
 
3.3%
30.01 1606
 
3.2%
29.93 1563
 
3.1%
29.92 1549
 
3.1%
29.95 1510
 
3.0%
29.97 1486
 
3.0%
Other values (60) 32851
65.7%
ValueCountFrequency (%)
29.635 1123
2.2%
29.64 22
 
< 0.1%
29.65 16
 
< 0.1%
29.66 13
 
< 0.1%
29.67 22
 
< 0.1%
29.68 34
 
0.1%
29.69 23
 
< 0.1%
29.7 41
 
0.1%
29.71 51
 
0.1%
29.72 67
 
0.1%
ValueCountFrequency (%)
30.315 539
1.1%
30.31 56
 
0.1%
30.3 82
 
0.2%
30.29 75
 
0.1%
30.28 97
 
0.2%
30.27 112
 
0.2%
30.26 85
 
0.2%
30.25 133
 
0.3%
30.24 136
 
0.3%
30.23 142
 
0.3%

Visibility(mi)
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing936
Missing (%)1.9%
Memory size781.2 KiB
10.0
49064 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters196256
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.0
2nd row10.0
3rd row10.0
4th row10.0
5th row10.0

Common Values

ValueCountFrequency (%)
10.0 49064
98.1%
(Missing) 936
 
1.9%

Length

2025-01-11T00:41:55.833964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-11T00:41:55.871154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10.0 49064
100.0%

Most occurring characters

ValueCountFrequency (%)
0 98128
50.0%
1 49064
25.0%
. 49064
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147192
75.0%
Other Punctuation 49064
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98128
66.7%
1 49064
33.3%
Other Punctuation
ValueCountFrequency (%)
. 49064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 196256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98128
50.0%
1 49064
25.0%
. 49064
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 196256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98128
50.0%
1 49064
25.0%
. 49064
25.0%

Wind_Direction
Categorical

MISSING 

Distinct24
Distinct (%)< 0.1%
Missing553
Missing (%)1.1%
Memory size781.2 KiB
Calm
10886 
West
5961 
WNW
3642 
Variable
3270 
SSW
3203 
Other values (19)
22485 

Length

Max length8
Median length5
Mean length3.7384068
Min length1

Characters and Unicode

Total characters184853
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCalm
2nd rowWest
3rd rowSSW
4th rowWest
5th rowNorth

Common Values

ValueCountFrequency (%)
Calm 10886
21.8%
West 5961
11.9%
WNW 3642
 
7.3%
Variable 3270
 
6.5%
SSW 3203
 
6.4%
South 3131
 
6.3%
WSW 3104
 
6.2%
SW 2961
 
5.9%
NW 2403
 
4.8%
North 1738
 
3.5%
Other values (14) 9148
18.3%

Length

2025-01-11T00:41:55.917642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
calm 11069
22.4%
west 5961
12.1%
wnw 3642
 
7.4%
variable 3270
 
6.6%
ssw 3203
 
6.5%
south 3131
 
6.3%
wsw 3104
 
6.3%
sw 2961
 
6.0%
nw 2403
 
4.9%
north 1738
 
3.5%
Other values (13) 8965
18.1%

Most occurring characters

ValueCountFrequency (%)
W 29595
16.0%
S 21363
11.6%
a 18655
10.1%
l 14156
 
7.7%
N 13231
 
7.2%
t 12059
 
6.5%
C 11069
 
6.0%
m 10886
 
5.9%
e 9231
 
5.0%
E 8993
 
4.9%
Other values (12) 35615
19.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 96594
52.3%
Uppercase Letter 88259
47.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 18655
19.3%
l 14156
14.7%
t 12059
12.5%
m 10886
11.3%
e 9231
9.6%
s 7190
 
7.4%
r 5008
 
5.2%
h 4869
 
5.0%
o 4869
 
5.0%
i 3270
 
3.4%
Other values (2) 6401
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
W 29595
33.5%
S 21363
24.2%
N 13231
15.0%
C 11069
 
12.5%
E 8993
 
10.2%
V 3333
 
3.8%
A 246
 
0.3%
L 183
 
0.2%
M 183
 
0.2%
R 63
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 184853
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 29595
16.0%
S 21363
11.6%
a 18655
10.1%
l 14156
 
7.7%
N 13231
 
7.2%
t 12059
 
6.5%
C 11069
 
6.0%
m 10886
 
5.9%
e 9231
 
5.0%
E 8993
 
4.9%
Other values (12) 35615
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 184853
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 29595
16.0%
S 21363
11.6%
a 18655
10.1%
l 14156
 
7.7%
N 13231
 
7.2%
t 12059
 
6.5%
C 11069
 
6.0%
m 10886
 
5.9%
e 9231
 
5.0%
E 8993
 
4.9%
Other values (12) 35615
19.3%

Wind_Speed(mph)
Real number (ℝ)

MISSING 

Distinct33
Distinct (%)0.1%
Missing11878
Missing (%)23.8%
Infinite0
Infinite (%)0.0%
Mean7.9640864
Minimum0
Maximum19.1
Zeros183
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-01-11T00:41:55.970586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.5
Q14.6
median6.9
Q310.4
95-th percentile16.1
Maximum19.1
Range19.1
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation3.8966001
Coefficient of variation (CV)0.48927145
Kurtosis0.34673755
Mean7.9640864
Median Absolute Deviation (MAD)2.3
Skewness0.89600742
Sum303606.9
Variance15.183492
MonotonicityNot monotonic
2025-01-11T00:41:56.027204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
3.5 5331
10.7%
4.6 5193
10.4%
5.8 4911
9.8%
6.9 4268
 
8.5%
8.1 3866
 
7.7%
9.2 3317
 
6.6%
11.5 2454
 
4.9%
10.4 2444
 
4.9%
12.7 1471
 
2.9%
13.8 1092
 
2.2%
Other values (23) 3775
 
7.5%
(Missing) 11878
23.8%
ValueCountFrequency (%)
0 183
 
0.4%
1 9
 
< 0.1%
1.2 22
 
< 0.1%
2 6
 
< 0.1%
2.3 46
 
0.1%
3 113
 
0.2%
3.5 5331
10.7%
4.6 5193
10.4%
5 93
 
0.2%
5.8 4911
9.8%
ValueCountFrequency (%)
19.1 759
1.5%
18.4 277
 
0.6%
18 3
 
< 0.1%
17.3 441
0.9%
17 7
 
< 0.1%
16.1 575
1.1%
16 11
 
< 0.1%
15 856
1.7%
14 14
 
< 0.1%
13.8 1092
2.2%

Weather_Condition
Categorical

IMBALANCE  MISSING 

Distinct39
Distinct (%)0.1%
Missing831
Missing (%)1.7%
Memory size781.2 KiB
Clear
28525 
Overcast
5123 
Mostly Cloudy
4439 
Partly Cloudy
4099 
Scattered Clouds
2855 
Other values (34)
4128 

Length

Max length28
Median length5
Mean length7.5004373
Min length3

Characters and Unicode

Total characters368789
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowClear
2nd rowClear
3rd rowClear
4th rowScattered Clouds
5th rowHaze

Common Values

ValueCountFrequency (%)
Clear 28525
57.0%
Overcast 5123
 
10.2%
Mostly Cloudy 4439
 
8.9%
Partly Cloudy 4099
 
8.2%
Scattered Clouds 2855
 
5.7%
Light Rain 1626
 
3.3%
Haze 1073
 
2.1%
Fair 525
 
1.1%
Rain 407
 
0.8%
Heavy Rain 110
 
0.2%
Other values (29) 387
 
0.8%
(Missing) 831
 
1.7%

Length

2025-01-11T00:41:56.085827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
clear 28525
45.6%
cloudy 8610
 
13.8%
overcast 5123
 
8.2%
mostly 4440
 
7.1%
partly 4101
 
6.6%
scattered 2855
 
4.6%
clouds 2855
 
4.6%
rain 2164
 
3.5%
light 1742
 
2.8%
haze 1073
 
1.7%
Other values (26) 1018
 
1.6%

Most occurring characters

ValueCountFrequency (%)
l 48595
13.2%
a 44512
12.1%
r 41225
11.2%
e 40688
11.0%
C 39990
10.8%
t 21161
 
5.7%
y 17274
 
4.7%
o 16148
 
4.4%
d 14359
 
3.9%
13337
 
3.6%
Other values (29) 71500
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 292962
79.4%
Uppercase Letter 62479
 
16.9%
Space Separator 13337
 
3.6%
Other Punctuation 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 48595
16.6%
a 44512
15.2%
r 41225
14.1%
e 40688
13.9%
t 21161
7.2%
y 17274
 
5.9%
o 16148
 
5.5%
d 14359
 
4.9%
s 12485
 
4.3%
u 11481
 
3.9%
Other values (12) 25034
8.5%
Uppercase Letter
ValueCountFrequency (%)
C 39990
64.0%
O 5123
 
8.2%
M 4462
 
7.1%
P 4108
 
6.6%
S 2981
 
4.8%
R 2164
 
3.5%
L 1742
 
2.8%
H 1186
 
1.9%
F 638
 
1.0%
D 54
 
0.1%
Other values (5) 31
 
< 0.1%
Space Separator
ValueCountFrequency (%)
13337
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 355441
96.4%
Common 13348
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 48595
13.7%
a 44512
12.5%
r 41225
11.6%
e 40688
11.4%
C 39990
11.3%
t 21161
 
6.0%
y 17274
 
4.9%
o 16148
 
4.5%
d 14359
 
4.0%
s 12485
 
3.5%
Other values (27) 59004
16.6%
Common
ValueCountFrequency (%)
13337
99.9%
/ 11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 368789
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 48595
13.2%
a 44512
12.1%
r 41225
11.2%
e 40688
11.0%
C 39990
10.8%
t 21161
 
5.7%
y 17274
 
4.7%
o 16148
 
4.4%
d 14359
 
3.9%
13337
 
3.6%
Other values (29) 71500
19.4%

Amenity
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size439.5 KiB
False
49615 
True
 
385
ValueCountFrequency (%)
False 49615
99.2%
True 385
 
0.8%
2025-01-11T00:41:56.131724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Bump
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size439.5 KiB
False
49975 
True
 
25
ValueCountFrequency (%)
False 49975
> 99.9%
True 25
 
0.1%
2025-01-11T00:41:56.167223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Crossing
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size439.5 KiB
False
46726 
True
 
3274
ValueCountFrequency (%)
False 46726
93.5%
True 3274
 
6.5%
2025-01-11T00:41:56.201159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Give_Way
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size439.5 KiB
False
49914 
True
 
86
ValueCountFrequency (%)
False 49914
99.8%
True 86
 
0.2%
2025-01-11T00:41:56.238644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Junction
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size439.5 KiB
False
44801 
True
5199 
ValueCountFrequency (%)
False 44801
89.6%
True 5199
 
10.4%
2025-01-11T00:41:56.273835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

No_Exit
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size439.5 KiB
False
49977 
True
 
23
ValueCountFrequency (%)
False 49977
> 99.9%
True 23
 
< 0.1%
2025-01-11T00:41:56.308330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Railway
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size439.5 KiB
False
49392 
True
 
608
ValueCountFrequency (%)
False 49392
98.8%
True 608
 
1.2%
2025-01-11T00:41:56.343121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Roundabout
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size439.5 KiB
False
49997 
True
 
3
ValueCountFrequency (%)
False 49997
> 99.9%
True 3
 
< 0.1%
2025-01-11T00:41:56.376550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Station
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size439.5 KiB
False
48421 
True
 
1579
ValueCountFrequency (%)
False 48421
96.8%
True 1579
 
3.2%
2025-01-11T00:41:56.410142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Stop
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size439.5 KiB
False
48313 
True
 
1687
ValueCountFrequency (%)
False 48313
96.6%
True 1687
 
3.4%
2025-01-11T00:41:56.445392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Traffic_Calming
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size439.5 KiB
False
49965 
True
 
35
ValueCountFrequency (%)
False 49965
99.9%
True 35
 
0.1%
2025-01-11T00:41:56.478987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Traffic_Signal
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size439.5 KiB
False
44589 
True
5411 
ValueCountFrequency (%)
False 44589
89.2%
True 5411
 
10.8%
2025-01-11T00:41:56.513226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Turning_Loop
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size439.5 KiB
False
50000 
ValueCountFrequency (%)
False 50000
100.0%
2025-01-11T00:41:56.551296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Start_Year
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2016.0
50000 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters300000
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016.0
2nd row2016.0
3rd row2016.0
4th row2016.0
5th row2016.0

Common Values

ValueCountFrequency (%)
2016.0 50000
100.0%

Length

2025-01-11T00:41:56.590839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-11T00:41:56.628541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016.0 50000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 100000
33.3%
2 50000
16.7%
1 50000
16.7%
6 50000
16.7%
. 50000
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 250000
83.3%
Other Punctuation 50000
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 100000
40.0%
2 50000
20.0%
1 50000
20.0%
6 50000
20.0%
Other Punctuation
ValueCountFrequency (%)
. 50000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 100000
33.3%
2 50000
16.7%
1 50000
16.7%
6 50000
16.7%
. 50000
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 100000
33.3%
2 50000
16.7%
1 50000
16.7%
6 50000
16.7%
. 50000
16.7%

Start_Month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.77376
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-01-11T00:41:56.665977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median8
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.273993
Coefficient of variation (CV)0.42115951
Kurtosis-0.49312049
Mean7.77376
Median Absolute Deviation (MAD)2
Skewness-0.67680263
Sum388688
Variance10.71903
MonotonicityNot monotonic
2025-01-11T00:41:56.712553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 6780
13.6%
8 6564
13.1%
9 5974
11.9%
10 5699
11.4%
12 5489
11.0%
7 5077
10.2%
1 4702
9.4%
6 3115
6.2%
4 2745
5.5%
5 1852
 
3.7%
Other values (2) 2003
 
4.0%
ValueCountFrequency (%)
1 4702
9.4%
2 208
 
0.4%
3 1795
 
3.6%
4 2745
5.5%
5 1852
 
3.7%
6 3115
6.2%
7 5077
10.2%
8 6564
13.1%
9 5974
11.9%
10 5699
11.4%
ValueCountFrequency (%)
12 5489
11.0%
11 6780
13.6%
10 5699
11.4%
9 5974
11.9%
8 6564
13.1%
7 5077
10.2%
6 3115
6.2%
5 1852
 
3.7%
4 2745
5.5%
3 1795
 
3.6%

Start_Day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.9476
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-01-11T00:41:56.762016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7626611
Coefficient of variation (CV)0.54946582
Kurtosis-1.192802
Mean15.9476
Median Absolute Deviation (MAD)8
Skewness-0.017034188
Sum797380
Variance76.78423
MonotonicityNot monotonic
2025-01-11T00:41:56.814857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 1919
 
3.8%
22 1885
 
3.8%
23 1841
 
3.7%
12 1781
 
3.6%
11 1744
 
3.5%
21 1741
 
3.5%
14 1718
 
3.4%
24 1711
 
3.4%
8 1698
 
3.4%
30 1691
 
3.4%
Other values (21) 32271
64.5%
ValueCountFrequency (%)
1 1559
3.1%
2 1434
2.9%
3 1394
2.8%
4 1594
3.2%
5 1919
3.8%
6 1552
3.1%
7 1687
3.4%
8 1698
3.4%
9 1549
3.1%
10 1483
3.0%
ValueCountFrequency (%)
31 970
1.9%
30 1691
3.4%
29 1633
3.3%
28 1453
2.9%
27 1601
3.2%
26 1659
3.3%
25 1621
3.2%
24 1711
3.4%
23 1841
3.7%
22 1885
3.8%

Start_Hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.52498
Minimum0
Maximum23
Zeros1122
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-01-11T00:41:56.877680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median14
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.7827264
Coefficient of variation (CV)0.42755896
Kurtosis-0.48949017
Mean13.52498
Median Absolute Deviation (MAD)4
Skewness-0.45477714
Sum676249
Variance33.439925
MonotonicityNot monotonic
2025-01-11T00:41:57.310019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11 3920
 
7.8%
20 3714
 
7.4%
10 3608
 
7.2%
19 3416
 
6.8%
18 3114
 
6.2%
12 3073
 
6.1%
16 2731
 
5.5%
14 2610
 
5.2%
9 2602
 
5.2%
13 2597
 
5.2%
Other values (14) 18615
37.2%
ValueCountFrequency (%)
0 1122
2.2%
1 989
 
2.0%
2 938
 
1.9%
3 736
 
1.5%
4 637
 
1.3%
5 748
 
1.5%
6 708
 
1.4%
7 1072
2.1%
8 1558
3.1%
9 2602
5.2%
ValueCountFrequency (%)
23 924
 
1.8%
22 1898
3.8%
21 2450
4.9%
20 3714
7.4%
19 3416
6.8%
18 3114
6.2%
17 2481
5.0%
16 2731
5.5%
15 2354
4.7%
14 2610
5.2%

Start_Weekday
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.55688
Minimum0
Maximum6
Zeros7956
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-01-11T00:41:57.352716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8016852
Coefficient of variation (CV)0.70464207
Kurtosis-0.92360223
Mean2.55688
Median Absolute Deviation (MAD)1
Skewness0.23855456
Sum127844
Variance3.2460696
MonotonicityNot monotonic
2025-01-11T00:41:57.394765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 8900
17.8%
2 8739
17.5%
1 8482
17.0%
4 8155
16.3%
0 7956
15.9%
5 4044
8.1%
6 3724
7.4%
ValueCountFrequency (%)
0 7956
15.9%
1 8482
17.0%
2 8739
17.5%
3 8900
17.8%
4 8155
16.3%
5 4044
8.1%
6 3724
7.4%
ValueCountFrequency (%)
6 3724
7.4%
5 4044
8.1%
4 8155
16.3%
3 8900
17.8%
2 8739
17.5%
1 8482
17.0%
0 7956
15.9%

Is_Weekend
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0.0
50000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters150000
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 50000
100.0%

Length

2025-01-11T00:41:57.442883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-11T00:41:57.480457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 50000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 100000
66.7%
. 50000
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100000
66.7%
Other Punctuation 50000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 100000
100.0%
Other Punctuation
ValueCountFrequency (%)
. 50000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 150000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 100000
66.7%
. 50000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 100000
66.7%
. 50000
33.3%

Interactions

2025-01-11T00:41:53.400718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:48.456253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.058014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.604480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.086507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.571677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:51.109448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:51.592718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.402596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.930934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.441827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:48.543076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.108150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.649269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.136371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.620361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:51.153763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:51.638168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.456251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.976645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.487740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:48.627622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.154132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.696838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.184170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.669888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:51.203070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:51.685712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.506903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.021968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.536098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:48.716363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.242790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.748140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.238914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.769680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:51.253505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.075858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.556953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.071253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.582925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:48.777586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.298299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.792522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.283872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.814952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:51.300874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.122699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.606509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.119309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.630198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:48.823896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.351471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.840851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.335801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.860832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:51.350262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.170424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.653782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.169101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.689929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:48.873803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.402668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.891699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.385361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.912011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:51.399896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.223062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.746131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.216735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.734923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:48.924981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.454188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.938123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.428542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.956335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:51.451258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.267811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.790567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.259659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.780272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:48.968618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.500920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.985262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.480020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:51.011120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:51.499954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.316254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.838533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.313898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.823135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.011516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:49.551896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.031108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:50.525415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:51.062270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:51.544397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.357919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:52.882960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-11T00:41:53.355540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-01-11T00:41:57.521330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AmenityBumpCrossingDistance(mi)Give_WayHumidity(%)JunctionNo_ExitPressure(in)RailwayRoundaboutSeveritySourceStart_DayStart_HourStart_LatStart_LngStart_MonthStart_WeekdayStationStopTemperature(F)Traffic_CalmingTraffic_SignalWeather_ConditionWind_DirectionWind_Speed(mph)
Amenity1.0000.0000.0890.0000.0320.0130.0290.0000.0080.0160.0000.0640.0000.0000.0140.0310.0380.0000.0050.1220.0490.0120.0100.0870.0330.0080.000
Bump0.0001.0000.0130.0000.0000.0050.0000.0000.0150.0000.0000.0000.0000.0070.0030.0250.0260.0200.0100.0080.0070.0000.8280.0000.0000.0000.020
Crossing0.0890.0131.0000.0010.0760.0180.0840.0060.0200.1340.0000.1560.0000.0110.0310.1000.1110.0410.0140.1530.1390.0190.0340.4280.0000.0260.011
Distance(mi)0.0000.0000.0011.0000.0000.1630.0350.0000.2190.0000.0000.0330.0160.0850.0370.0620.0640.4680.0220.0100.0380.3030.0000.0000.1740.1380.078
Give_Way0.0320.0000.0760.0001.0000.0150.0130.0000.0120.0000.0000.0370.0000.0070.0000.0570.0370.0120.0070.0000.0790.0140.0070.0440.0000.0150.000
Humidity(%)0.0130.0050.0180.1630.0151.0000.0360.0050.1240.0170.0000.0300.006-0.060-0.096-0.000-0.116-0.0140.0230.0300.012-0.6990.0050.0130.2080.100-0.148
Junction0.0290.0000.0840.0350.0130.0361.0000.0040.0300.0050.0090.0990.0000.0000.0170.1220.1370.0460.0090.0410.0530.0360.0030.1010.0380.0420.034
No_Exit0.0000.0000.0060.0000.0000.0050.0041.0000.0000.0000.0000.0000.0000.0070.0080.0140.0050.0090.0000.0000.0190.0000.0000.0000.0070.0270.000
Pressure(in)0.0080.0150.0200.2190.0120.1240.0300.0001.0000.0100.0100.0220.019-0.104-0.0970.063-0.0990.205-0.0710.0330.022-0.3980.0150.0040.2380.216-0.179
Railway0.0160.0000.1340.0000.0000.0170.0050.0000.0101.0000.0000.0160.0000.0000.0000.0370.0720.0000.0000.2930.0040.0240.0060.0100.0290.0240.006
Roundabout0.0000.0000.0000.0000.0000.0000.0090.0000.0100.0001.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0040.0000.0000.0000.0000.0000.000
Severity0.0640.0000.1560.0330.0370.0300.0990.0000.0220.0160.0001.0000.0000.0090.0220.1180.1430.0300.0140.0550.1210.0350.0040.2000.0500.0360.016
Source0.0000.0000.0000.0160.0000.0060.0000.0000.0190.0000.0000.0001.0000.0000.0220.0700.0620.0130.0040.0000.0000.0300.0000.0000.0000.0090.000
Start_Day0.0000.0070.0110.0850.007-0.0600.0000.007-0.1040.0000.0000.0090.0001.0000.006-0.0160.015-0.074-0.0120.0000.0000.0090.0070.0160.0780.0360.029
Start_Hour0.0140.0030.0310.0370.000-0.0960.0170.008-0.0970.0000.0000.0220.0220.0061.000-0.0120.0340.002-0.0440.0250.0000.0950.0040.0280.0910.1330.102
Start_Lat0.0310.0250.1000.0620.057-0.0000.1220.0140.0630.0370.0000.1180.070-0.016-0.0121.000-0.782-0.1250.0210.0960.098-0.1940.0330.0690.2470.2030.117
Start_Lng0.0380.0260.1110.0640.037-0.1160.1370.005-0.0990.0720.0000.1430.0620.0150.034-0.7821.0000.077-0.0380.1170.0790.2200.0300.0990.1920.140-0.124
Start_Month0.0000.0200.0410.4680.012-0.0140.0460.0090.2050.0000.0000.0300.013-0.0740.002-0.1250.0771.0000.0210.0210.016-0.0580.0170.0080.1380.121-0.163
Start_Weekday0.0050.0100.0140.0220.0070.0230.0090.000-0.0710.0000.0040.0140.004-0.012-0.0440.021-0.0380.0211.0000.0000.004-0.0290.0000.0100.0590.0320.043
Station0.1220.0080.1530.0100.0000.0300.0410.0000.0330.2930.0000.0550.0000.0000.0250.0960.1170.0210.0001.0000.0680.0430.0040.1790.0130.0580.016
Stop0.0490.0070.1390.0380.0790.0120.0530.0190.0220.0040.0040.1210.0000.0000.0000.0980.0790.0160.0040.0681.0000.0270.0130.0510.0000.0220.017
Temperature(F)0.0120.0000.0190.3030.014-0.6990.0360.000-0.3980.0240.0000.0350.0300.0090.095-0.1940.220-0.058-0.0290.0430.0271.0000.0000.0140.1980.1120.133
Traffic_Calming0.0100.8280.0340.0000.0070.0050.0030.0000.0150.0060.0000.0040.0000.0070.0040.0330.0300.0170.0000.0040.0130.0001.0000.0110.0000.0140.026
Traffic_Signal0.0870.0000.4280.0000.0440.0130.1010.0000.0040.0100.0000.2000.0000.0160.0280.0690.0990.0080.0100.1790.0510.0140.0111.0000.0250.0120.012
Weather_Condition0.0330.0000.0000.1740.0000.2080.0380.0070.2380.0290.0000.0500.0000.0780.0910.2470.1920.1380.0590.0130.0000.1980.0000.0251.0000.1650.154
Wind_Direction0.0080.0000.0260.1380.0150.1000.0420.0270.2160.0240.0000.0360.0090.0360.1330.2030.1400.1210.0320.0580.0220.1120.0140.0120.1651.0000.338
Wind_Speed(mph)0.0000.0200.0110.0780.000-0.1480.0340.000-0.1790.0060.0000.0160.0000.0290.1020.117-0.124-0.1630.0430.0160.0170.1330.0260.0120.1540.3381.000

Missing values

2025-01-11T00:41:53.923090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-11T00:41:54.169037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-11T00:41:54.421574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SourceSeverityStart_LatStart_LngDistance(mi)StreetTemperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityBumpCrossingGive_WayJunctionNo_ExitRailwayRoundaboutStationStopTraffic_CalmingTraffic_SignalTurning_LoopStart_YearStart_MonthStart_DayStart_HourStart_WeekdayIs_Weekend
53187Source2334.152000-118.1324770.01N Lake AveNaNNaNNaNNaNNaNNaNNaNFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.012.07.017.02.00.0
39792Source2238.654404-121.3083190.00Dewey Dr71.646.029.98010.0CalmNaNClearFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalse2016.05.03.010.01.00.0
88886Source2334.081623-117.7193300.00I-10 E73.461.029.90010.0West10.4ClearFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.08.04.021.03.00.0
38433Source2337.595280-121.8725740.00I-680 S86.028.029.94010.0SSW4.6ClearFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse2016.05.018.011.02.00.0
11816Source2237.746010-121.5841670.01Altamont Pass Rd42.182.030.31510.0West3.5Scattered CloudsFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse2016.01.026.08.03.00.0
11468Source2337.688770-122.1353530.01Nimitz Fwy S60.183.030.19010.0NorthNaNHazeFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse2016.011.014.011.00.00.0
97847Source2234.402683-118.4549870.00Via Princessa77.046.029.63510.0VAR3.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.08.024.010.02.00.0
88926Source2233.960163-118.2563930.00Manchester Ave69.181.030.00010.0WSW8.1ClearFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalse2016.08.02.021.01.00.0
96220Source2333.809055-118.2874760.00Sepulveda Blvd64.456.030.02010.0South6.9Scattered CloudsFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.03.030.011.02.00.0
16862Source2238.487099-121.3719480.01Elk Grove Florin Rd64.456.030.09010.0CalmNaNMostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.011.01.014.01.00.0
SourceSeverityStart_LatStart_LngDistance(mi)StreetTemperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityBumpCrossingGive_WayJunctionNo_ExitRailwayRoundaboutStationStopTraffic_CalmingTraffic_SignalTurning_LoopStart_YearStart_MonthStart_DayStart_HourStart_WeekdayIs_Weekend
64909Source2234.136494-117.4279790.01Foothill Fwy W46.476.029.8510.0East5.8OvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.01.023.015.00.00.0
6902Source2238.578369-121.3080440.00US-50 E71.653.029.9810.0South5.8Partly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.07.08.011.04.00.0
25882Source2237.274483-122.0026470.00Norman Y Mineta Hwy64.950.030.1410.0NW10.4Partly CloudyFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse2016.09.030.011.04.00.0
75895Source2334.135372-117.9862900.01Foothill Fwy E57.233.030.1910.0NNE6.9ClearFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.011.024.00.03.00.0
3660Source2237.257042-121.9313280.01Union Ave60.160.030.1310.0NW8.1Mostly CloudyFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.012.013.011.01.00.0
61141Source2334.321411-118.4967500.01I-5 N50.087.029.97NaNESE17.3Heavy RainFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.01.022.01.06.00.0
28494Source2337.709053-122.1643910.00I-880 N64.075.030.1110.0WNW11.5Scattered CloudsFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.08.024.011.02.00.0
94639Source2334.303253-118.4797740.00Roxford St80.645.029.8610.0CalmNaNClearFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.08.031.022.02.00.0
23197Source2337.595844-122.0590290.00Nimitz Fwy S64.948.030.1410.0West17.3ClearFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.09.030.015.04.00.0
20637Source2238.496521-121.4463270.00CA-99 N71.151.030.0410.0Variable3.5ClearFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse2016.09.015.010.03.00.0

Duplicate rows

Most frequently occurring

SourceSeverityStart_LatStart_LngDistance(mi)StreetTemperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityBumpCrossingGive_WayJunctionNo_ExitRailwayRoundaboutStationStopTraffic_CalmingTraffic_SignalTurning_LoopStart_YearStart_MonthStart_DayStart_HourStart_WeekdayIs_Weekend# duplicates
86Source2334.070206-117.8646160.01I-10 E77.039.029.9710.0West11.5ClearFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.010.027.013.03.00.03
0Source2232.654800-117.0930710.01CA-54 W69.165.030.0810.0WNW8.1OvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.010.012.010.02.00.02
1Source2232.718796-117.1177220.01CA-15 N55.093.029.9010.0CalmNaNMostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.011.028.021.00.00.02
2Source2232.751488-117.2050630.00Rosecrans St77.069.029.8910.0NW12.7Scattered CloudsFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalse2016.07.026.013.01.00.02
3Source2232.825104-116.9597240.01Pepper Dr57.2100.030.0910.0NorthNaNOvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.01.011.010.02.00.02
4Source2232.953854-117.1893310.01CA-56 E57.074.030.1310.0NNW5.8Mostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.01.03.019.01.00.02
5Source2233.127144-117.1048050.00CA-78 W77.076.029.9210.0West8.1ClearFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.07.027.011.02.00.02
6Source2233.192944-117.2688900.00CA-78 W75.976.029.9010.0CalmNaNClearFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.07.021.018.03.00.02
7Source2233.579433-117.2135390.00Ristras Ln55.441.030.2210.0CalmNaNClearFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.01.025.014.02.00.02
8Source2233.687443-117.8720170.00I-405 N72.078.029.7810.0SSW10.4Scattered CloudsFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2016.07.029.019.04.00.02